Multi-view SAR Image Classification through Decision Fusion of Adaptive Dictionary Learning and CNN

This paper introduces a decision fusion strategy for automatic target recognition in multi-view synthetic aperture radar (SAR) images. Our proposed architecture integrates decisions derived from adaptive dictionary learning and Convolutional Neural Network (CNN) methods. Specifically, we employ two...

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Bibliographic Details
Published in2024 Photonics & Electromagnetics Research Symposium (PIERS) pp. 1 - 7
Main Authors Wang, Liyuqi, Tang, Mengjiao, Rong, Yao, Ni, Meixin, Li, Fan
Format Conference Proceeding
LanguageEnglish
Published IEEE 21.04.2024
Subjects
Accuracy
Adaptation models
Adaptive systems
Dictionaries
Feature extraction
Machine learning
Radar polarimetry
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Summary:This paper introduces a decision fusion strategy for automatic target recognition in multi-view synthetic aperture radar (SAR) images. Our proposed architecture integrates decisions derived from adaptive dictionary learning and Convolutional Neural Network (CNN) methods. Specifically, we employ two adaptive dictionary learning methods with distinct dictionary constructions to extract features from SAR images at each viewpoint, leading to intermediate decisions. Simultaneously, the CNN branch utilizes a multi-input single-output fully CNN to extract features from multiple viewpoints and make an intermediate decision. Subsequently, considering the multi-view images and leveraging the strengths of different methods, the classification results produced by these models are aggregated through voting to yield the final classification labels. Experimental results on the MSTAR dataset demonstrate the effectiveness of our proposed method, achieving an outstanding accuracy of 99.958% in a ten-class classification task.
ISSN:2831-5804
DOI:10.1109/PIERS62282.2024.10618786